Content recommendation systems are widely used and are typically of two basic types, collaborative filtering and content-based filtering. Content-based filtering techniques typically require very little user input but are limited in their scope. Collaborative filtering can be much more powerful, but often requires much more information on the user. When end users want to use a content recommendation system, they are often asked to submit or select a list of their interests, so that the system can find relevant content for them. This is time-consuming and often imprecise, since people often select topics they think they should be interested in, rather than topics they actually read about.